Update app.py

agent.py

@@ -138,6 +138,19 @@ sentence_transformer.max_seq_length = 512  # Set max sequence length
 # Initialize embeddings with the model name (dim=768)
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 
+
+try:
+    results = retriever.get_relevant_documents("What is vector search?")
+    if not results:
+        raise ValueError("No documents found in the search results.")
+    # Access the first result safely if it exists
+    first_result = results[0]
+    print("First result:", first_result)
+except Exception as e:
+    print(f"Error: {e}")
+
+
+
 # Initialize Supabase client
 supabase: Client = create_client(
     os.environ.get("SUPABASE_URL"),